ACHPCorrelations.py 文件源码

def KM_Cond_Average(x_min,x_max,Ref,G,Dh,Tbubble,Tdew,p,beta,satTransport=None):
    """
    Returns the average pressure gradient and average heat transfer coefficient
    between qualities of x_min and x_max.
    for Kim&Mudawar two-phase condensation in mico-channel HX

    To obtain the pressure gradient for a given value of x, pass it in as x_min and x_max

    Required parameters:
    * x_min : The minimum quality for the range [-]
    * x_max : The maximum quality for the range [-]
    * Ref : String with the refrigerant name
    * G : Mass flux [kg/m^2/s]
    * Dh : Hydraulic diameter of tube [m]
    * Tbubble : Bubblepoint temperature of refrigerant [K]
    * Tdew : Dewpoint temperature of refrigerant [K]
    * beta: channel aspect ratio (=width/height)

    Optional parameters:
    * satTransport : A dictionary with the keys 'mu_f','mu_g,'rho_f','rho_g', 'sigma' for the saturation properties.  So they can be calculated once and passed in for a slight improvement in efficiency
    """

    def KMFunc(x):
        dpdz, h = Kim_Mudawar_condensing_DPDZ_h(Ref,G,Dh,x,Tbubble,Tdew,p,beta,satTransport)
        return dpdz , h

    ## Use Simpson's Rule to calculate the average pressure gradient
    ## Can't use adapative quadrature since function is not sufficiently smooth
    ## Not clear why not sufficiently smooth at x>0.9
    if x_min==x_max:
        return KMFunc(x_min)
    else:
        #Calculate the tranport properties once
        satTransport={}
        satTransport['rho_f']=PropsSI('D','T',Tbubble,'Q',0.0,Ref)
        satTransport['rho_g']=PropsSI('D','T',Tdew,'Q',1.0,Ref)
        satTransport['mu_f']=PropsSI('V','T',Tbubble,'Q',0.0,Ref)
        satTransport['mu_g']=PropsSI('V','T',Tdew,'Q',1.0,Ref)
        satTransport['cp_f']=PropsSI('C', 'T', Tbubble, 'Q', 0, Ref)
        satTransport['k_f']=PropsSI('L', 'T', Tbubble, 'Q', 0, Ref)

        #Calculate Dp and h over the range of xx
        xx=np.linspace(x_min,x_max,30)
        DP=np.zeros_like(xx)
        h=np.zeros_like(xx)
        for i in range(len(xx)):
            DP[i]=KMFunc(xx[i])[0]
            h[i]=KMFunc(xx[i])[1]

        #Use Simpson's rule to carry out numerical integration to get average DP and average h
        if abs(x_max-x_min)<5*machine_eps:
            #return just one of the edge values
            return DP[0], h[0]
        else:
            #Use Simpson's rule to carry out numerical integration to get average DP and average h
            return -simps(DP,xx)/(x_max-x_min), simps(h,xx)/(x_max-x_min)